Job Summary
The AI Engineer will serve as a hands-on technical lead for an Agile engineering squad, owning solution architecture, technical direction, code quality, security, and production delivery of AI-powered knowledge solutions. The role combines software engineering, Generative AI, large language models (LLMs), retrieval-augmented generation (RAG), agentic workflows, cloud technologies, and technical leadership. The engineer will contribute directly to coding while coaching team members, removing delivery blockers, managing technical dependencies, and ensuring scalable, secure, maintainable, and production-ready solutions.
Key Responsibilities
• Own the squad's solution architecture and technical design, balancing functional requirements with security, operational resilience, scalability, usability, performance, cost, and component reuse.
• Define technical direction, establish implementation approaches, identify architectural risks, and translate product requirements into technical specifications and designs.
• Collaborate with Product Owners and Product Analysts on backlog refinement, story mapping, technical priorities, and acceptance criteria.
• Remain hands-on with software development, contributing to implementation while leading, coaching, and unblocking engineers.
• Design and develop services, REST APIs, and microservices using Python and FastAPI.
• Build and integrate Generative AI and LLM-based applications, including RAG solutions, agentic search, workflows, and retrieval and synthesis over large document collections.
• Develop front-end components and applications using ReactJS.
• Build and deploy cloud-based solutions using AWS and Azure, including Azure OpenAI and commercial LLMs such as Claude.
• Design and implement enterprise integrations, architecture standards, and SaaS solutions that meet organizational requirements.
• Develop and optimize Elasticsearch indexing pipelines, search relevance, and search performance.
• Establish and maintain pull request review processes, coding standards, automated code quality checks, and appropriate test coverage.
• Apply AI-assisted code review before human review and establish guardrails for AI-generated code, ensuring validation against specifications, architecture, security requirements, tests, and coding standards.
• Minimize technical debt, obtain appropriate agreement before accepting new debt, and prioritize existing technical debt with the Product Owner.
• Implement CI/CD and DevSecOps practices, including code quality and security scanning with tools such as SonarQube and Veracode.
• Lead application testing, diagnostics, performance tuning, deployment execution, and production readiness for a global user base.
• Use agentic engineering and specification-driven development to define clear goals, context, constraints, plans, tasks, and acceptance criteria for AI coding agents.
• Apply coding agents throughout the software lifecycle, including implementation, testing, code review, documentation, and maintenance, while retaining accountability for technical decisions and production quality.
• Remove technical blockers, coordinate dependencies within and across teams, support escalations, and improve engineering delivery speed and workflow efficiency.
• Coach and mentor engineers, provide direct and constructive feedback, promote collaboration, and foster a team culture that supports experimentation, learning, and continuous improvement.
• Support team morale and work-life balance while encouraging accountability and shared engineering standards.
• Collaborate with business stakeholders to understand product objectives and communicate technical concepts clearly to engineering teams and non-technical stakeholders.
• Share technical knowledge through engineering sessions, technical discussions, and cross-team forums.
• Maintain clear technical documentation, architecture artifacts, and implementation guidance.
Required Qualifications
• 7+ years of professional software engineering experience, including responsibility for solution architecture in complex enterprise environments.
• 1+ years of hands-on experience building Generative AI applications, including RAG solutions, agentic search, workflows, and integrations with commercial LLMs.
• 2+ years of experience leading or mentoring engineers.
• 2+ years of experience operating as a senior or principal engineer, setting technical direction through design, implementation, and operational ownership.
• 2+ years of experience leading engineering work within Agile teams using Scrum or Kanban, collaborating with Product Owners, Product Analysts, and Quality Engineers.
• Experience translating business requirements into technical specifications, architectural designs, and implementation plans.
• Strong experience designing and building Python services using FastAPI, including REST APIs and microservices.
• Experience with front-end development using ReactJS.
• Experience developing cloud-based applications using AWS and Azure, including Azure OpenAI and other LLMs such as Claude.
• Experience building GenAI and LLM-based applications for retrieval, search, synthesis, and workflows involving large document collections.
• Knowledge of systems integration, enterprise architecture standards, and SaaS solution selection and integration.
• Experience with Elasticsearch development, indexing pipelines, relevance tuning, and search performance optimization.
• Understanding of CI/CD and DevSecOps practices, including code quality and security scanning tools such as SonarQube and Veracode.
• Experience with application testing, diagnostics, performance tuning, and production readiness.
• Ability to produce clear technical documentation and architecture artifacts.
• Experience with agentic engineering, specification-driven development, and the effective use of AI coding agents across the software development lifecycle.
• Strong technical leadership, problem-solving, prioritization, and decision-making skills, including the ability to manage ambiguity and make sound architectural decisions.
• Excellent written and verbal communication skills, with the ability to explain technical concepts to engineers and business stakeholders.
• Ability to balance hands-on coding with technical leadership, coaching, collaboration, and delivery ownership.
• Ability to build productive relationships and work effectively in fast-paced, multicultural, service-oriented, and distributed teams.
Preferred Qualifications
• AWS or Azure cloud certification.
• Experience staying current with Generative AI research, emerging technologies, and modern AI-assisted engineering practices.
• Experience improving engineering productivity, delivery flow, and software quality through automation and effective technical practices.
Certifications
• AWS or Azure cloud certification preferred.
